On deriving influences of upwind agricultural and anthropogenic emissions on greenhouse gas concentrations and air quality over Delhi in India: A stochastic Lagrangian footprint approach

Abstract

Delhi, the capital city of India witnesses severe degradation of air quality and rapid enhancement of trace gases during winter. Still it is unclear about the relative role of the meteorological conditions and the post-monsoon agricultural stubble burning on the occurrence of these events. To overcome this, we examine the use of applying high-resolution transport model to establish the link between atmospheric concentrations and upstream surface fluxes. This study reports the implementation of a Lagrangian approach and demonstrates its capability in deriving the upwind influences over Delhi. We simulate stochastic back trajectories over Delhi by implementing stochastic time-inverted Lagrangian transport (STILT) model, driven by the meteorological fields from the European Centre for Medium Range Weather Forecasts (ECMWF) model. During the post-monsoon, when mixing layer height is shallow, we find high near-field influence. The variations in footprint simulations with receptor heights show the effect of mixing layer dynamics on the air-parcels. By using atemporal emission fields, we find a considerable impact of meteorological conditions during November that contributes to the enhancements of trace gases. Together with strong emissions (anthropogenic and biomass burning), these enhancements can be several orders higher compared to other seasons. Through the receptor-oriented STILT implementation over India, we envision a wide range of applications spanning from air quality to climate change. An advantage of this implementation is that it allows the use of pre-calculated footprints in simulating any trace gas species and particulate matter, making it computationally less demanding than running an ensemble of full atmospheric transport model.